Evidence map›Paper›PMID 36759513›Full record

ArticleNature communications2023

A method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data.

Md Moksedul Momin, Jisu Shin, Soohyun Lee, Buu Truong, Beben Benyamin, S Hong Lee

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Revisiting the Trans-Ancestry Genetic Correlation of Refractive Error.Investigative ophthalmology & visual science · 2025
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Controlling for polygenic genetic confounding in epidemiologic association studies.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  15. Article
  16. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Md Moksedul MominAustralian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.ORCID 0000-0002-3512-2257
Jisu ShinAustralian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.ORCID 0000-0001-9376-8251
Soohyun LeeDivision of Animal Breeding and Genetics, National Institute of Animal Science (NIAS), Cheonan, South Korea.
Buu TruongAustralian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.ORCID 0000-0003-0043-0812
Beben BenyaminAustralian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.ORCID 0000-0001-5608-2293
S Hong LeeAustralian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia. Hong.Lee@unisa.edu.au.ORCID 0000-0001-9701-2718

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is diverse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific allelic effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to anthropometric and other complex traits from the UK Biobank data across ancestry groups. For obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestries.

Indexed as

AnthropometryGenetics, PopulationGenome-Wide Association StudyMultifactorial InheritanceBlack PeopleGene FrequencyHumansPolymorphism, Single NucleotideUnited KingdomWhite People

Identifiers

PMID36759513
PMCPMC9911789

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.